import sys import pytest import torch from sglang.jit_kernel.set_mla_kv_buffer import ( can_use_set_mla_kv_buffer, set_mla_kv_buffer, ) from sglang.jit_kernel.utils import get_ci_test_range from sglang.test.ci.ci_register import register_cuda_ci register_cuda_ci(est_time=30, suite="base-b-kernel-unit-1-gpu-large") DEVICE = "cuda" CACHE_SIZE = 4096 # (nope_dim, rope_dim) pairs: standard MLA, MLA scale buffer, FP8 nope-extended layout. SHAPES = get_ci_test_range( [(512, 64), (512, 32), (256, 64), (128, 64), (528, 64)], [(512, 64), (528, 64)], ) BATCH_SIZES = get_ci_test_range([1, 7, 64, 257, 1024], [1, 64, 1024]) def _ref(kv_buffer, loc, cache_k_nope, cache_k_rope): nope_dim = cache_k_nope.shape[-1] n_loc = loc.shape[0] src_nope = cache_k_nope.reshape(n_loc, -1) src_rope = cache_k_rope.reshape(n_loc, -1) kv_view = kv_buffer.view(kv_buffer.shape[0], -1) kv_view[loc.long(), :nope_dim] = src_nope kv_view[loc.long(), nope_dim : nope_dim + src_rope.shape[-1]] = src_rope @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16]) @pytest.mark.parametrize("shape", SHAPES) @pytest.mark.parametrize("batch_size", BATCH_SIZES) def test_set_mla_kv_buffer_correctness(dtype, shape, batch_size): nope_dim, rope_dim = shape total_dim = nope_dim + rope_dim cache_k_nope = torch.randn((batch_size, 1, nope_dim), dtype=dtype, device=DEVICE) cache_k_rope = torch.randn((batch_size, 1, rope_dim), dtype=dtype, device=DEVICE) kv_buffer = torch.randn((CACHE_SIZE, 1, total_dim), dtype=dtype, device=DEVICE) kv_ref = kv_buffer.clone() loc = torch.randperm(CACHE_SIZE, device=DEVICE)[:batch_size] set_mla_kv_buffer(kv_buffer, loc, cache_k_nope, cache_k_rope) _ref(kv_ref, loc, cache_k_nope, cache_k_rope) assert torch.equal(kv_buffer, kv_ref) @pytest.mark.parametrize("loc_dtype", [torch.int32, torch.int64]) def test_set_mla_kv_buffer_loc_dtypes(loc_dtype): nope_dim, rope_dim = 512, 64 batch_size = 128 dtype = torch.bfloat16 cache_k_nope = torch.randn((batch_size, 1, nope_dim), dtype=dtype, device=DEVICE) cache_k_rope = torch.randn((batch_size, 1, rope_dim), dtype=dtype, device=DEVICE) kv_buffer = torch.randn( (CACHE_SIZE, 1, nope_dim + rope_dim), dtype=dtype, device=DEVICE ) kv_ref = kv_buffer.clone() loc = torch.randperm(CACHE_SIZE, device=DEVICE)[:batch_size].to(loc_dtype) set_mla_kv_buffer(kv_buffer, loc, cache_k_nope, cache_k_rope) _ref(kv_ref, loc, cache_k_nope, cache_k_rope) assert torch.equal(kv_buffer, kv_ref) def test_set_mla_kv_buffer_uint8_byte_layout(): """FP8 DSA byte-layout: cache_k_nope is uint8 with [fp8(512) | scales(16)] = 528, cache_k_rope is uint8 [128]; total payload = 656 bytes.""" nope_bytes, rope_bytes = 528, 128 batch_size = 64 dtype = torch.uint8 cache_k_nope = torch.randint( 0, 256, (batch_size, 1, nope_bytes), dtype=dtype, device=DEVICE ) cache_k_rope = torch.randint( 0, 256, (batch_size, 1, rope_bytes), dtype=dtype, device=DEVICE ) kv_buffer = torch.randint( 0, 256, (CACHE_SIZE, 1, nope_bytes + rope_bytes), dtype=dtype, device=DEVICE ) kv_ref = kv_buffer.clone() loc = torch.randperm(CACHE_SIZE, device=DEVICE)[:batch_size] set_mla_kv_buffer(kv_buffer, loc, cache_k_nope, cache_k_rope) _ref(kv_ref, loc, cache_k_nope, cache_k_rope) assert torch.equal(kv_buffer, kv_ref) def test_set_mla_kv_buffer_empty_loc(): nope_dim, rope_dim = 512, 64 dtype = torch.bfloat16 cache_k_nope = torch.empty((0, 1, nope_dim), dtype=dtype, device=DEVICE) cache_k_rope = torch.empty((0, 1, rope_dim), dtype=dtype, device=DEVICE) kv_buffer = torch.randn( (CACHE_SIZE, 1, nope_dim + rope_dim), dtype=dtype, device=DEVICE ) kv_before = kv_buffer.clone() loc = torch.empty((0,), dtype=torch.int64, device=DEVICE) set_mla_kv_buffer(kv_buffer, loc, cache_k_nope, cache_k_rope) assert torch.equal(kv_buffer, kv_before) def test_can_use_set_mla_kv_buffer(): assert can_use_set_mla_kv_buffer(1024, 128) # bf16 (512,64) assert can_use_set_mla_kv_buffer(528, 128) # fp8 byte layout assert not can_use_set_mla_kv_buffer(13, 8) # not multiple of 4 if __name__ == "__main__": sys.exit(pytest.main([__file__, "-v", "-s"]))